Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	20,186
2	New Jersey	19,042
3	Massachusetts	15,532
4	Rhode Island	15,421
5	District of Columbia	14,197
6	Connecticut	12,833
7	Delaware	11,065
8	Illinois	10,902
9	Maryland	10,735
10	Louisiana	10,731
11	Nebraska	9,206
12	Iowa	8,247
13	Arizona	7,235
14	South Dakota	7,117
15	Mississippi	7,063
16	Michigan	6,796
17	Virginia	6,794
18	Pennsylvania	6,719
19	Indiana	6,460
20	Alabama	6,122
21	Minnesota	5,842
22	Georgia	5,791
23	Utah	5,479
24	Colorado	5,300
25	Arkansas	5,156
26	Tennessee	5,083
27	North Carolina	5,039
28	New Mexico	5,038
29	South Carolina	4,795
30	Florida	4,529
31	California	4,525
32	North Dakota	4,322
33	Nevada	4,297
34	Wisconsin	4,279
35	Kansas	4,207
36	New Hampshire	4,077
37	Texas	3,962
38	Washington	3,912
39	Ohio	3,833
40	Kentucky	3,115
41	Missouri	3,022
42	Oklahoma	2,657
43	Idaho	2,252
44	Maine	2,199
45	Wyoming	2,068
46	Puerto Rico	2,043
47	Vermont	1,857
48	Oregon	1,646
49	West Virginia	1,418
50	Alaska	1,174
51	Montana	670
52	Hawaii	566

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Arizona	409
2	South Carolina	203
3	Arkansas	180
4	Florida	176
5	Utah	161
6	North Carolina	140
7	Texas	140
8	Alabama	123
9	Nevada	116
10	Iowa	112
11	Georgia	108
12	Tennessee	103
13	California	98
14	Oklahoma	97
15	Delaware	94
16	Minnesota	73
17	South Dakota	70
18	Nebraska	68
19	Virginia	68
20	New Mexico	65
21	Maryland	58
22	Missouri	57
23	District of Columbia	55
24	Illinois	54
25	Idaho	52
26	Indiana	50
27	Wisconsin	50
28	Washington	49
29	Ohio	48
30	Oregon	45
31	Puerto Rico	43
32	Kansas	42
33	North Dakota	41
34	Kentucky	39
35	New Jersey	38
36	New York	37
37	Colorado	36
38	Pennsylvania	32
39	Massachusetts	30
40	Wyoming	30
41	Alaska	29
42	Connecticut	29
43	Michigan	29
44	Louisiana	27
45	New Hampshire	23
46	West Virginia	23
47	Rhode Island	21
48	Maine	19
49	Montana	19
50	Hawaii	12
51	Vermont	12
52	Mississippi	0

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New York	1,587
2	New Jersey	1,448
3	Connecticut	1,194
4	Massachusetts	1,139
5	Rhode Island	843
6	District of Columbia	755
7	Louisiana	667
8	Michigan	610
9	Illinois	541
10	Maryland	507
11	Pennsylvania	505
12	Delaware	446
13	Indiana	377
14	Mississippi	316
15	Colorado	286
16	Minnesota	250
17	New Hampshire	249
18	Georgia	245
19	Ohio	230
20	New Mexico	223
21	Iowa	217
22	Virginia	188
23	Arizona	185
24	Alabama	171
25	Washington	166
26	Missouri	158
27	Nevada	158
28	Florida	147
29	California	139
30	Nebraska	130
31	Wisconsin	127
32	South Carolina	126
33	Kentucky	121
34	North Carolina	118
35	North Dakota	108
36	Oklahoma	93
37	South Dakota	91
38	Vermont	89
39	Kansas	88
40	Maine	75
41	Tennessee	75
42	Texas	75
43	Arkansas	74
44	Idaho	60
45	Utah	49
46	West Virginia	49
47	Puerto Rico	46
48	Oregon	45
49	Wyoming	34
50	Montana	18
51	Alaska	13
52	Hawaii	12

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Massachusetts	4
2	Connecticut	3
3	Idaho	3
4	Illinois	3
5	Arizona	2
6	District of Columbia	2
7	Indiana	2
8	Louisiana	2
9	Maryland	2
10	Minnesota	2
11	New Jersey	2
12	New Mexico	2
13	New York	2
14	Rhode Island	2
15	South Carolina	2
16	Alabama	1
17	Arkansas	1
18	California	1
19	Delaware	1
20	Florida	1
21	Georgia	1
22	Michigan	1
23	Nebraska	1
24	Nevada	1
25	New Hampshire	1
26	North Carolina	1
27	Ohio	1
28	Pennsylvania	1
29	South Dakota	1
30	Washington	1
31	Wisconsin	1
32	Wyoming	1
33	Alaska	0
34	Colorado	0
35	Hawaii	0
36	Iowa	0
37	Kansas	0
38	Kentucky	0
39	Maine	0
40	Mississippi	0
41	Missouri	0
42	Montana	0
43	North Dakota	0
44	Oklahoma	0
45	Oregon	0
46	Puerto Rico	0
47	Tennessee	0
48	Texas	0
49	Utah	0
50	Vermont	0
51	Virginia	0
52	West Virginia	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	129,918	1	99
Lake	Tennessee	98,204	2	99
Dakota	Nebraska	87,436	3	99
Buena Vista	Iowa	84,964	4	99
Lincoln	Arkansas	79,545	5	99
Davidson	Tennessee	11,600	224	92
Richland	South Carolina	6,434	538	82
Orange	California	3,282	1092	65
York	South Carolina	2,897	1210	61
Pierce	Washington	2,863	1224	61

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	3,784	1	99
Randolph	Georgia	3,393	2	99
Terrell	Georgia	3,165	3	99
Early	Georgia	3,042	4	99
Northampton	Virginia	2,391	5	99
Richland	South Carolina	190	642	79
Davidson	Tennessee	137	804	74
Pierce	Washington	103	956	69
Orange	California	85	1068	66
York	South Carolina	39	1483	52

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.